Influence of spatial and temporal variation on establishing stable isotope baselines of <scp>δ<sup>15</sup>N</scp>, <scp>δ<sup>13</sup>C</scp>, and <scp>δ<sup>34</sup>S</scp> in a large freshwater lake
Bibliographic record
Abstract
Abstract It is essential to establish a baseline in studies using stable isotopes to interpret trophic relationships across ecosystems and through time. Studies in freshwater ecosystems struggle to quantify baseline stable isotopes due to difficulties collecting representative samples, particularly from pelagic habitats. We assessed temporal and spatial variation in δ13C, δ15N, and δ34S in a commonly used pelagic baseline, seston (n = 156), in Lake Erie to understand mechanisms that correlate with baseline stable isotope dynamics in large lakes. Seston contains a wide range of material which can confound stable isotope interpretation, and we examined the utility of element content and ratios to account for variation in sample source. Seston was collected in each of the three basins of Lake Erie from May to October in 2017–2019 at nearshore (<10 m depth) and offshore (>10 m depth) sites. General linear models were conducted on each stable isotope (δ15N, δ13C, and δ34S) and sample composition (variables: %N, %C, %S, C:N, C:S, and N:S) to assess how basin, month, and collection year influenced seston stable isotopes and composition. Sample composition (variables: %N, %C, %S, C:N, C:S, and N:S), which is rarely reported for organisms in stable isotope studies, was constant throughout the sample period with no temporal or spatial trends except for small variations in %C, C:N, C:S, and N:S. This indicated that the temporal and spatial trends observed within the stable isotopes were related to seasonal changes in system processes and plankton community dynamics, with few or minimal changes in the amount of detrital and inorganic material within seston. Values and trends of δ15N, δ13C, and δ34S in seston were comparable to those measured previously in Lake Erie and other Laurentian Great Lakes. All three isotopes increased from May to October of each sample year and varied spatially, δ15N was higher, δ34S was lower, and δ13C was the same in the west basin compared to the central and eastern basins of Lake Erie, which did not differ. These trends probably reflect seasonal changes in plankton community composition and nutrient cycling throughout the lake and are potentially linked to the presence of Microcystis blooms in the western basin during the late summer and autumn. Seston turns over quickly, as shown by the rapid changes in stable isotope values throughout the study, which confounds the investigation of stable isotopes in upper trophic levels, and especially in organisms that have slower tissue turnover and move throughout the lake seasonally. Additionally, the variable composition of seston (e.g., % C, % N, % S, C:N, C:S, N:S) necessitates analysing sample composition to determine the degree of abiotic (e.g., detritus, sediment, particulate organic matter) and biotic (e.g., phytoplankton, zooplankton) content in it.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".